A framework for mapping industrial emergence R. Phaal*, E. O’Sullivan, M. Routley, S. Ford and D. Probert *Corresponding author Institute for Manufacturing, University of Cambridge 17 Charles Babbage Road, Cambridge, CB3 0FS, UK Tel: +44 (0)1223 765828, email:
[email protected]
Abstract The industrial landscape is becoming increasingly complex and dynamic, with innovative technologies stimulating the emergence of new applications, business models and industries. This paper presents a framework for mapping science-based industrial emergence, in order to better understand the nature and characteristics of such phenomena, as a basis for improved strategy development. A full lifecycle perspective is included, emphasising early stage phases associated with scientific and technological developments, together with key transitions between phases related to the conversion of scientific knowledge to technological capability and application. Roadmapping concepts are used to map industrial emergence phenomena from various perspectives that cover value creation and capture activities together with demand and supply side factors. The framework has been tested by developing more than 20 diverse ‘quick scan’ maps of historical industrial emergence, building confidence that the framework might be applicable to current and future emergence. Common characteristics of industrial emergence have been identified, including key events and milestones, focusing on a chain of demonstrators that delineate the various phases and transitions.
Keywords: industrial dynamics; roadmapping
1.
Introduction
Many managers and policy makers are aware of the strategic importance of science and technology in delivering value and competitive advantage to their organisations, industrial networks, regions and nations. Such groups should be aware of the dynamics associated with the emergence of technologies, markets and organisations if they are to be effective. These issues are becoming more critical as the cost, complexity and rate of technology change increase, and competition and sources of technology globalise. Both managers and policy makers (funding agencies) have to make decisions about which technological areas to focus investment on and which strategic options to pursue. Such decisions are challenging, owing to the complex and dynamic nature of market, technological and industrial developments taking place, compounded by scarcity of information and uncertainty of forecasts. Considerable time can elapse between key decisions to fund technology developments, and the economic, social or environmental benefits sought through the exploitation of the technology in applications, products and services. While many specific processes and tools are available to support organisations develop and implement innovation strategies, there is a need for holistic, integrating frameworks that can provide context and support for navigating this turbulent process from initial concept to -1-
market, throughout the industry lifecycle. Porter [1] defines emerging industries as “newly formed or re-formed industries that have been created by technological innovations, shifts in relative cost relationships, emergence of new consumer needs, or other economic and sociological changes that elevate a new product or service to the level of a potentially viable business opportunity”. This highlights the broad scope of issues that need to be considered, while also focusing on specific factors that enable or hinder emergence process. The framework presented in this paper is based on the application of roadmapping concepts to historical industrial emergence [2], focusing on technology-intensive product-based sectors. The aim is to improve understanding of the dynamics and characteristics of emergence, with the intention of developing approaches that can be applied to inform strategy and decision making for current and future industrial emergence, at both firm and public policy levels. More than 20 ‘quick scan’ maps of historical industrial emergence have been created to support the development and testing of the framework. These have been selected to cover a diverse range of industrial domains, applications, technological and scientific areas, and phases of industrial emergence: synthetic diamond, mobile phone, digital camera, medical imaging (including MRI, ultrasound, tomography and X-ray), software, computer, osteosynthesis, catalytic converter, battery, cheese, automotive, stem cells, portable entertainment, low-temperature technology, displays (TFT-LCD), wireless communications, ink jet printing, glass fibre, regenerative medicine and semiconductors. The conceptual foundations for the proposed framework for industrial emergence are reviewed in Section 2 and the framework is described in detail in Section 3, illustrated with reference to one particular case in Section 4 (synthetic diamond). The paper concludes with a discussion of the patterns of industrial emergence that have been observed, and recommendations for further development and application of the framework.
2.
Conceptual foundations
The scope of industrial emergence is large, covering many areas of research and practice. The following sections focus on three key perspectives that underpin the proposed framework for mapping industrial emergence: 1) patterns of industrial dynamics, with particular reference to time-based models; 2) the underlying principles of emergence, relating to the behaviour of complex systems and their evolution; and 3) the technology roadmapping approach used as a basis for mapping industrial emergence. 2.1
Patterns of industrial dynamics
There is an increasing awareness that the interaction between science and industry is an important aspect of the innovation ecology [3]. Information available on the topic of emerging industries is disparate and dispersed amongst several fields of research [4,5], Economics [6], marketing [7], socio-technical systems [8,9,10], and industrial dynamics [11] have all published models and theories relating to industrial evolution. However these focus on evolution rather than emergence [4]. Within the models of industry evolution, knowledge of the early stages is much sparser than within the later stages [12]. Describing the nature of industrial emergence is made all the more challenging by a lack of consensus about how to define the term ‘industry’. Several authors have acknowledged this difficulty [6,1], that the nature of an industry evolves over time [13], and the definition is linked to perception and cognitive frames [14]. It can be difficult to define exactly when an industry begins, and specifically what stage an industry is currently in [15], as “all firms within an industry need not be in the same stage of the industry lifecycle” [16]. Due to the complex coupling within industrial emergence, the process is not linear, as described in -2-
traditional economics literature, but instead there are many iterations and interactions between different factors [17,18]. To simplify the understanding of the dynamics involved within industrial emergence, several time-based models have been proposed, breaking down industrial evolution into phases or stages within a ‘lifecycle’ [19,20,21,13]. Amongst publications in this area, there is confusion and overlap between different terminology used, such as ‘product lifecycle’ and ‘industry lifecycle’ [1], or industry lifecycle and the market or industry evolution cycle [16]. Most lifecycle models divide into stages, with a recognised difficulty in defining the boundaries of each stage. Often turning points in the rate of growth of sales are used [13], however this is difficult to apply in an emerging industry, where the definition of the industry, and the sales data can be impossible to access. Typically the industry lifecycle has four phases: introduction, growth, maturity and decline. Jacobsson and Bergek [22] define two phases – formative and growth, however this level of granularity is not particularly useful for studying industrial emergence. Suarez [23] defines five phases in the process of technological dominance: R&D build-up, technical feasibility, creating the market, decisive battle and postdominance; emphasising that both firm- and environmental-level factors are important at each phase. Meijer et al. [24] discuss the different perceived uncertainties seen by different groups of actors when transitioning from the earliest phase. Key characteristics of emerging industries are uncertainty [25], the fact that different groups are involved in different phases [24], and the need for organisational structures [26] and competitive strategies [20] to evolve through the phases. To assist with progression through the different phases and navigation across the transitions, there are several factors which can act as enablers; similarly there will be barriers, which will need to be removed or reduced [27]. Due to the non-linear and situated character of technological developments, there is little point in generic advice [9], but each case will be contextual, industry-specific, and related to the phase of emergence in which the industry or firm is present. The interaction between market (demand-side) and technological (supplyside) forces and events is a key driver for industrial emergence, as a co-evolutionary process. This is explored further in the next section. 2.2
Emergent behaviour in complex evolutionary systems
The dynamic patterns of industrial emergence discussed in the previous section are the result of complex interacting and adaptive processes. Two key theoretical perspectives provide a basis for understanding the behaviours observed in the emergence maps: industry as a complex system, and industrial emergence as an evolutionary process. While our understanding of evolution owes great debts to the biological sciences, there is now broad appreciation that its application extends to the socio-economic domain [28,29,30] and that evolution is a ‘generic generative mechanism’ explaining processes of change in a wide variety of systems [31]. Evolution can be understood as the discrete operations of variation generation, selection, inheritance and competition. However, unlike biological evolution, variations seen in industrial evolution are not blind, but rather are a result of complex coupling interactions between cognitive, economic and social factors [32]. To understand the dynamics involved within emerging industries it is important not to focus solely on a demand pull or technology push perspective, but to adopt an approach which concentrates on the interaction of both these effects [33,34], and the complex interactions between agents and processes that give rise to co-evolutionary dynamics and emergent behaviours. Variation is the source of evolution and what makes a system dynamic [35]. Variety in socioeconomic systems is generated by entrepreneurs and organisations by creating new products, processes, markets and organisational forms through processes of discovery and -3-
recombination [36]. This process is not blind or random; agents anticipate market selection forces in the pursuit of competitive advantage and adapt their strategies as a result of these interactions[37,35]. Those innovations and firms that are able to adapt to their selection environment have greater ‘fitness’ and are retained; those that fail to adapt to the environment are eliminated [38,31]. The emergence maps enable the decisions, activities and achievements of the key actors to be depicted (organisations and individuals), including both market and technological events and their interactions, and how businesses succeed or fail over time. This is illustrated in Section 4 for the emergence of the synthetic diamond industry, showing how individual actions and decisions play into the broader sweep of industrial competition and development. Mechanisms for generating microdiversity at the lower level of the system select for systems that retain these mechanisms within them [39]. Accordingly, it can be seen that variety generation and selection mechanisms are mutually influential. Through a process of adaptation, the selection environment shapes the development of innovations by entrepreneurs and firms, while the variety generated in turn shapes the selection environment [40]. As March describes “… the convergence between an evolving unit and its environment is complicated by the fact that the environment is not only changing but changing partly as part of a process of coevolution. There is mutual adaptation between the unit of evolution and the environment” [41]. The reciprocal interactions between agents and the strategies of agents lead to coevolutionary, complex systems [35]. Complex behaviour is caused by nonlinear interactions, with the components of the system being irreducible from those at lower levels of the system [42]. Such systems adapt to their external environment, generating coherent structure without the intervention of a centralised form of internal control but through a process of selforganisation [43]. The capacity of complex systems to self-organise “…enables them to develop or change internal structure spontaneously and adoptively in order to cope with, or manipulate, their environment” [44]. Self-organisation represents a search for attractor ‘solutions’ within a complex system [45]. Describing the ‘crystallization’ of massively disordered systems to a very high degree of order, Kauffman [46] comments how “much of the order we see … may be the direct result not of natural selection but of the natural order selection was privileged to act on”. Order emerges from unpredictable interactions between entities in physical and socio-economic systems. This emergence can be described as occurring through two stages; the correlation of entities with one another through interaction, followed by the catalysed aggregation of the initial interaction [47]. Self-organisation arises through the result of a complex interaction between the environment, the present state of the system and the history of the system [44]. The history of a system is important for understanding further self-organisation and coevolution because complex systems exhibit path dependence [48,49]. Existing technologies and institutions are an important precondition for novelty because they provide the basis for devices and techniques to be modified, along with a rich set of intellectual resources that can be applied to new settings [50]. Schumpeter was alert to this fact because he saw innovation as occurring through the recombination of existing products, processes, materials and organizational forms [36]. Similarly, in describing how innovations are a product of what has come before, Metcalfe [31] comments that “the creation of novelty involves guided variation within perceptions of a limited set of possibilities. Innovations are never entirely novel; they are always prefigured in some of their dimensions”. New technologies and industries can therefore be understood as resulting from the existing structure of opportunities and constraints, with path dependency meaning that initial conditions can have significant impact on the trajectory followed by a technology-based industry [51,4].
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The emergence maps depict the dynamics of how industries evolve and develop, including growth, consolidation, maturity, decline and failure. New technologies enable waves of application development and market activity, with periods of convergence and stability, and periods of rapid change. The synthetic diamond case shows how two firms came to dominate an industry, following a path-dependent trajectory influenced by technological and market developments and events. 2.3
Technology roadmapping
Technology roadmapping provides a structured approach for mapping the evolution of complex systems, and the technique is widely applied at both firm and sector levels to support innovation, strategy and policy development and deployment [52-63]. Previous research in the application of roadmapping [64,65] has demonstrated the flexibility of the approach, leading to a generalised framework for strategic appraisal, illustrated in Fig. 1. Time is represented explicitly on most roadmaps, which take a forward (future) view, thus providing an holistic framework within which integrated strategy can be represented.
> Fig. 1 – Roadmapping framework
Roadmap frameworks generally comprise two key axes which both require configuration for application to the mapping of industrial emergence: time on the horizontal axis and a set of themes or perspectives represented on the vertical axis. Roadmaps can be thought of as ‘dynamic systems frameworks’ [66], enabling the development and evolution of complex industrial systems to be mapped. Robinson and Propp [5] have applied roadmapping principles to mapping science and technology-based industrial emergence, focusing on a specific application in the field of micro and nanotechnology. However, studies such as this are hampered by the high level of uncertainty associated with the future, involving speculation about how the industrial system might evolve. Extending the time axis to include the past enables the roadmapping technique to be used to map historical activities in a way that is compatible with future strategy, as demonstrated by Mills et al. [67] for manufacturing strategy, enabling key learning points to be identified and included in strategic deliberations. The framework described in this paper is an extension of roadmapping concepts to include principles, structure and content that accounts for the nature of industrial emergence, identified through a combination of literature review and mapping of historical maps. It is worth noting that there is little evidence in the literature of the extension of roadmapping methods to map and learn from past events in order to inform future strategy, although many simple linear timelines have been produced. This concept was tested as part of a broader research project [68], creating a map of how silicon gyroscope systems were developed and exploited within an aerospace company. This exercise was the inspiration for the mapping approach being developed for industrial emergence, underpinned by the framework presented in this paper.
3.
Framework for mapping industrial emergence
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The framework for mapping industrial emergence incorporates three main elements, compatible with roadmapping principles and methods, described in detail below: 1) Phases and transitions associated with science and technology-based industrial emergence, associated with the horizontal axis of the mapping framework. 2) Key themes that are required to represent industrial emergence, including demand and supply side drivers, together with value creation and capture activities and systems, associated with the vertical axis of the mapping framework. 3) Significant events and milestones that demarcate the progression from science to value creation associated with industrial emergence, coded into the content of the maps.
3.1
Phases and transitions of industrial emergence
Defining the phases and transitions associated with industrial emergence is important for understanding the underlying dynamics, and for providing improved guidance for organisations concerned with current emergence. Figure 2 summarises the key phases and transitions that have been observed in the mapping studies, reflecting the management and investment focus in terms of technology, application and markets, associated with the general pattern of content observed in the maps.
> Fig. 2 – Phases, transitions, milestones and trajectories of technology-intensive industrial emergence
The industry lifecycle shown in Fig. 2 is similar to many others that have been proposed in the literature (see Section 2.1), but with the following additions / features: An emphasis on the early stages of industrial evolution, associated with emergence, including a ‘precursor’ phase to represent the scientific developments that act as the initial conditions for industrial emergence, and an ‘embryonic’ phase associated with the translation of applied science proof-of-concept demonstrators into technology prototypes and early application demonstrators. A focus on the transitions between phases (science-technology, technology-application and application-market). These transitions are of particular interest, as they are associated with significant shifts in perspective and stakeholder interests, but are rarely emphasised in industry lifecycle models. The identification of particular milestones (demonstrators) that delineate the various phases and transitions. Although all phases and transitions of industrial emergence generally have some level of activity and interaction between technology, application and market, the different phases of the funnel tend to be progressively dominated by events related to science (S), technology (T), application (A) and market (M). The proposed phases and transitions of industrial emergence are: 1) Precursor phase (science-dominated, S): observing underpinning scientific phenomena through to the first demonstration of applied science potential, which stimulates industrial interest and investment. 2) Science-technology transition (S-T): Translating the potential of science into technology, showing that it is sufficiently robust to be integrated into a functional system. 3) Embryonic phase (technology-dominated, T): improving the reliability and performance of the technology to a point where it can be demonstrated in the field. 4) Technology-application transition (T-A): developing the technology and application to a point where commercial potential can be demonstrated through revenue generation.
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5) Nurture phase (application-dominated, A): improving the price and performance of the application to a point where sustainable business potential can be demonstrated. 6) Application-market transition (A-M): translating price-performance demonstrators into a market with mass growth potential. 7) Growth phase (market-dominated, M): marketing, commercial and business development leading to achieve sustainable industrial growth. 8) Maturity phase: refining established applications, production processes and business models. 9) Decline / renewal phase: the industry either declines (through competitive disruption) or is renewed through the development of new science-based technologies that repeat the above phases. It should be noted that the boundaries between these phases and transitions may be ‘blurred’, as a result of aggregating activities in sub-sectors and the many firms involved in an industry. This may not be apparent in historical maps, where the ‘lead wave’ is mapped and the ‘winners’ are clear with hindsight. It should also be noted that the patterns of emergence and the particular events and milestones that characterise this behaviour will depend on the type of sector – for example, if the industry is influenced significantly by regulation, or if the application is process intensive. As highlighted in Fig. 2, it is expected that the framework will lead to a better understanding of the factors that can accelerate or retard emergence for each phase and transition (opportunities, enablers, threats, barriers – internal and external to the industrial sector), and associated success factors. These provide the potential for creating an ‘early warning system’ for managers and policy makers to support strategic decision-making, depending on the position in the S-T-A-M progression, the characteristics of the sector they operate within and other contextual factors. Furthermore, the boundaries of the phases and transitions described above point to key stages of emergence at which managers and policy makers might use as review and decision-making points for filtering ideas, refining projects and programmes, prioritising investments, and developing policies and strategies.
3.2
Thematic representation of industrial emergence
The systems thinking that underpins roadmapping enables the approach to be adapted to industrial emergence in a way that can be configured to suit both industry- (sector) and firmlevel perspectives, as shown in Fig. 3, which emphasises the need to clearly define the system boundary (unit of analysis). The primary focus for most of the maps described in this paper is an industrial sector defined on the basis of a class of application or product. However, the unit of analysis tends to shift to a technology focus at earlier stages of emergence, associated with the key scientific and technological achievements that stimulate industrial emergence.
> Fig. 3 – Themes, phases and transitions for mapping industrial emergence (firm-level perspective)
The system is divided into value creation and value capture sub-systems, subject to internal and external demand and supply side drivers: Value creation: capabilities, activities processes and business systems that generate the potential for value generation, such as research, development, design, procurement and supply, production, sales and marketing, distribution and after-sales support. Value capture: mechanisms through which potential value is translated into actual value (sales and revenue) through delivery of benefits to customers, consumers and society, including products and services. -7-
Demand side drivers: external and high level internal factors that affect value capture and industrial emergence, including: business context, strategy and business models, together with market and industrial factors and dynamics, such as social, economic, environmental, political and legal trends and drivers, together with regulation, standards, customer and competitor behaviour. Supply side drivers: internal and external resources and other supply side enablers that support value creation and industrial emergence, including: finance, facilities, enabling technologies, skills, organisation, management, business processes, partnerships, networks and actions that can influence demand side enablers (such as lobbying and engagement in standards committees).
The framework needs to be customised in each case to represent the set of themes and perspectives that are important, providing a flexible and scaleable framework for mapping industrial emergence. For example, if regulation plays a particularly important role, as is often the case in the medical sector, then this should be reflected in the vertical axis of the framework.
3.3
Characterising industrial emergence
The phases, transitions and themes described above provide a ‘canvas’ (horizontal and vertical axes) for mapping industrial emergence. The ‘narrative’ represented by the content of the maps incorporates notable events, milestones and other factors that can be used to characterise and demarcate the emergence process (see Fig. 2). A chain of demonstrators delineates the phases and transitions, signalling key achievements that generate interest and potential investment in subsequent stages: Science demonstrators: demonstration of fundamental new scientific knowledge, normally via experiment and / or theory. Generally disseminated via peer-reviewed primary academic literature and academic conferences. Often the basis for scientists making the case for further scientific research funding from public research agencies, in order to develop fundamental understanding, leading potentially towards useful application. Associated with the science-dominated (S) precursor phase. Applied science demonstrators: demonstration of the feasibility that the scientific phenomenon has potential practical application, normally via proof-of-concept experiments. Generally disseminated through mechanisms such as peer-reviewed scientific / applied science journals and conferences. Often the basis for applied scientists to obtain industrial and public funds to explore the potential exploitation of the research. Associated with the end of the science-dominated (S) precursor phase and beginning of the science-technology (S-T) transition. Technology demonstrators: creation of a prototype demonstrating that the technology is sufficiently robust to be integrated into a functional system. Depending on the system environment complexity of the technology and / or application, prior “technical demonstrators” may be necessary within the technology-dominated phase in order to address system-deployment issues (for example, systems components and modules). Often the basis for technology champions to gain further funding for technology development. Associated with the end of the S-T transition and the beginning of the technology-dominated (T) embryonic phase. Application demonstrators: shows the potential functional benefits of a system under non-laboratory conditions. Depending on the complexity of the application environment, prior application demonstrators (“field demonstrators”) may be necessary to address environmental and / or real world performance issues. Often the basis for application champions to make the case for investment in product development. Associated with the
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end of the technology-dominated (T) embryonic phase and beginning of the technologyapplication (T-A) transition. Commercial application demonstrators: sustainable business venture with market growth potential. Depending on the complexity of the market environment (such as required levels of customer sophistication), additional commercial application demonstrators (for example, demonstrating issues such as manufacturability, market acceptance and regulatory compliance) may be necessary within the application-dominated phase in order to address any potential commercialisation / market-readiness concerns of investors. Associated with the end of the end of the technology-application (T-A) transition and beginning of the application-dominated (A) nurture phase. Price-performance market demonstrators: demonstrate the feasibility of a mass market, in terms of price and performance. Often the basis for application champions to gain alignment of business resources (for example, production and marketing). Associated with the end of the application-dominated (T) nurture phase and beginning of the application-market (A-M) transition. Mass market demonstrators: demonstration of substantial industry, market and business growth. Associated with the end of the application-market (A-M) transition and beginning of the market-dominated (M) growth phase.
In addition to demonstrators, other factors are important for characterising and enabling industrial emergence, including: Precursor market activity: existing market activities that may influence the entry of new technology-based applications (for instance, by demonstrating market demand for analogous products or advancing core technologies within different markets). Specialist markets: evidence of customised sales within limited markets (for example, consulting services and provision of specialist research equipment). Generally associated with highly customised sales to specialist customers (such as national defence agencies, research hospitals and university research departments). May be associated with any phase or transition, although early activity is of particular interest as evidence of potential value. Early adopter market: evidence of sales to sophisticated users who will pay a premium for new or improved functionality and performance. Associated with applicationdominated (A) nurture phase. Market stimuli: demand side market drivers that accelerate technology and application developments in the sector of interest, including both internal (within the industry) and external (other sector) factors. Technological stimuli: supply side technological drivers that significantly accelerate industrial emergence in the sector of interest, including both internal (within the industry) and external (other sector) factors. Enablers: any activity, event or process that advances or accelerates industrial emergence. Barriers: any activity, event or process that stops or significantly inhibits industrial emergence. Regional perspectives: regional and national activity relating to value creation and capture, of particular interest from a policy perspective.
4.
Case study
The use of the framework for mapping industrial emergence is illustrated below by means of a case study, focusing on the synthetic diamond industry. The framework has been developed on the basis of multiple diverse cases – each case does not necessarily represent all of the features of the framework. The synthetic diamond case has been selected for this case study
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owning to its relative simplicity, allowing the map and associated narrative to be represented in full. Synthetic diamond is widely used in manufacturing for grinding, machining and cutting processes, as well as other applications, due to its unique mechanical, thermal, chemical and optical properties. The industry has been largely dominated by Divisions of General Electric and De Beers, both part of substantial corporations. The case takes a company-level view, based on an interview with a senior manager (retired) of De Beers Industrial Diamond Division, who was personally involved in both the early research and subsequent development of the business and industry, from the 1950-1990s. Further work would be needed to provide a more comprehensive view of this industry (for example, GE and customer perspectives). The discussion was guided by the structure of the framework, forming the basis for the industrial emergence map shown in Fig. 4. This personal perspective was supplemented with information extracted from a published historical account of the early scientific advances in diamond synthesis [69].
> Fig. 4 – Emergence map for synthetic diamond industry
Precursor phase A long history of precursor activities preceded the first synthesis of diamond, which can be traced back to the 1772 when the French chemist Lavoisier demonstrated that diamond was composed of carbon (science demonstration). Diamond deposits were discovered in extinct volcanoes in South Africa in the late 19th century, demonstrating that diamonds could potentially be made in the laboratory under conditions of high pressure and temperature. Many subsequent experiments failed, as the apparatus of the time was not capable of reaching the required levels of pressure and temperature (of the order of 50,000 atmospheres and 1,000oC), and nobody knew what these targets were. The key breakthroughs came with a combination of: 1) Development of a theoretical phase diagram that defined the graphite-diamond transition, published by Rossini and Jessop in 1938 (science demonstration). 2) Development of high-pressure experimental equipment by Prof Percy Bridgman at Harvard University, culminating in a Nobel Prize in 1946 (applied science demonstration). Embryonic phase Aware of the above developments, several companies began serious applied research programmes to synthesise diamond. General Electric was widely credited with this first achievement in 1954 (technology demonstration), leading to the first commercial sales in 1956-7 of a limited quantity of low quality material (application demonstration). However, ASEA (a Swedish electricity company) actually achieved the first successful synthesis of diamond in 1953, without publicising the fact. During the same period, De Beers actively pursued research into the properties of diamond, with interests in both gem and industrial diamond applications (there was already a market for natural diamond in industrial applications, which emerged in the 1940s). In 1947 Sir Ernest Oppenheimer founded the Diamond Research Laboratory (DRL) in Johannesburg, building a formidable competence in the physics, chemistry and properties of diamond. - 10 -
Stimulated by the success of GE (and the threat it posed to its diamond business), De Beers began an accelerated programme of research into diamond synthesis, resulting in success in 1959 (70 carats), using high pressure presses developed by ASEA (De Beers later acquired the ASEA Division). Volume production started in South Africa in 1960, and in Ireland three years later, with the first commercial sales in 1961. Nurture phase Although both GE and De Beers had successfully synthesised diamond, this was only on a small scale in the laboratory. The process had to be scaled up to produce commercially viable quantities of diamond at a competitive price (commercial application demonstration). Since there was already a market for natural industrial diamonds (precursor market activity), there were few barriers to adoption of the new synthetic substitute in existing markets. However, synthetic diamond has considerable advantages in terms of both quality and reliability, including the ability to tailor diamond properties for specific (and new) applications. Also, once scale-up challenges were overcome (price-performance demonstration), there were few practical limits to the volume that could be produced. As markets expanded (mass market demonstration), the position of natural diamond in industry applications became almost irrelevant. GE invested heavily in production expansion and R&D, challenging the De Beers natural industrial diamond market, establishing a successful business by the late 1960s. However, the De Beers initiative had a slower start. Pilot plants were established in South Africa and Ireland in the early 1960s (taking advantage of the special tax benefits available in the Shannon region). Significant scale-up and production problems were encountered in the early years (De Beers, essentially a mining company, had little experience in manufacturing). To compound these difficulties, a management decision was taken to disband the successful research team. This turned out to be a very costly decision as it left GE with very little competition for almost a decade. Growth phase Management changes in De Beers in the early 1970s resulted in substantial re-investment and expansion of its research activities. However, it took many years to close the 10-year lead GE had acquired in both ultra high-pressure engineering and market position. Developments in numerical modelling technology (finite element analysis) and control systems played a major role in developing a new generation of high-pressure systems (external technological stimulus). By this time the demand for industrial diamonds had far exceeded the availability of natural diamond. Both GE and De Beers invested heavily in stimulating and expanding the market (internal market stimulus), including sponsoring conferences and participating in trade shows. The tool making industry is inherently conservative, and it was necessary to adopt a risksharing model where diamond was supplied free of charge, collaborative development programmes were established, and favourable payment terms were provided to stimulate toolmakers to adopt the new materials. The investment that De Beers had made in R&D started to pay off, catching up with GE and then taking the lead in terms of both technology and product portfolio. By the late 1980s De Beers had established a leading position in the market place. Maturity phase
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By this stage, both GE and De Beers synthetic diamond businesses were firmly established, and the industry could be considered to be mature. The main technical advance during this period was the development of composite diamond products, where diamond grit is fused together using small amounts of metal solvent. This lead to a tough, abrasion resistant material that could be produced cheaply in a variety of shapes and sizes covering many applications from the automotive industry to woodworking, ceramics, glass, oil drilling and mining. Decline / renewal phase By the late 1990s synthetic diamond could be considered as a commodity product, leading to pressure on profits. GE divested its synthetic diamond business (seen as non-core, and also not of a scale that fitted with its corporate strategy). The business was sold to a private equity firm, which was not successful, and the business was eventually acquired by Sandvik (a Swedish abrasives company). For many years, De Beers were aware of developments in a potential alternative low-pressure technology that might be used for synthesising diamond (chemical vapour deposition – CVD). The first attempts to synthesise diamond using this technology were made in Europe as early as 1911 (applied science demonstration). In the late 1980s it was considered that the technology was sufficiently advanced to warrant a major R&D effort, leading to synthesis in 1989 (technology demonstration). A research programme was initiated and rapid progress was made, resulting in the first commercial product in 1992 (commercial application demonstration). This has lead to substantial new business opportunities. As the diamond can be grown in the form of large sheets it is possible to produce a wide variety of products from surgical knives and cutting tools to optical windows at low cost (specialist / early adopter markets). The synthetic diamond case illustrates how the mapping approach can be used to depict and describe the events and interactions associated the emergence of an industry from the science base. The structure and features of the framework are based a diverse set of such cases, which collectively cover a wide range of emergence phenomena. For example, other cases have illustrated more complexity in terms of the number of organisations involved, applications and technologies; the role regulation and standards; direct consumer interaction; impact of government subsidy and policies; and the influence of exogenous events (demand- and supply-side).
5.
Conclusions
The framework presented in this paper provides a means for mapping industrial emergence in technology-intensive sectors, as a basis for improved understanding and communication of the complex dynamics associated with such systems. The framework is coherent with theoretical perspectives, and has been developed and tested through application in a wide range of industrial sectors and contexts. The approach has been demonstrated to be flexible and scaleable, where the focus can be on any ‘value creation and capture’ system, at levels ranging from entire industrial sectors (S-TA-M) to particular firm-level innovations (s-t-a-m). Defining the focal industrial system and its boundaries (the unit of analysis) is important, to understand the dynamic ‘push’ and ‘pull’ (supply and demand) forces acting on the system. The framework is broad in scope, enabling the evolution of complex industrial systems to be mapped across the full lifecycle of industrial emergence, with a focus on science, technology, application and market demonstrators as key milestones that demarcate the phases and transitions of emergence.
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The creation of multiple emergence maps for a range of diverse sectors, contexts and units of analysis illustrate a range of phenomena and patterns associated with technology-intensive industrial emergence:
Funnel-like patterns of emergence: the variety and number of events related to scientific and technological development decline over time as promising technological ideas are filtered and focused on successful applications and products. Uncertainties associated with both technological and commercial aspects decrease as the industry emerges. This industry-level funnel is analogous to well-known firm-level product development and open innovation funnel models that account for firm-level interactions with the external industry sector [70,71]. Funding associated with the phases and transitions of the industrial emergence framework are somewhat similar to ‘stage-gate’ models that are widely used to manage innovation and new product development at the firm level [72,73], and thus the framework has the potential for structuring funding agency decision making processes at the sector-level.
Transitions as key characteristics of industrial emergence: although many lifecycle frameworks identify phases, few highlight the importance of the transitions between these, or the specific demonstration milestones that delineate them. Exceptions include Rogers’ ‘Valley of death’ [74] and Moore’s ‘Chasm’ [75], and the ‘milestones of technological dominance’ identified by Suarez [23]. These transitions are characterised by changes in focus, activity and funding [24], and the emphasis on demonstrators of various types linked to the phase and transition boundaries provides a set of tangible intermediate milestones that may help to overcome these barriers.
Push-pull engine of industrial emergence: demand and supply side factors, and their interactions, are primary drivers that impact on the process of industrial emergence [33]. Demonstrations provide a vehicle for linking these perspectives, enabling positive feedback that influences the direction of both technological and commercial activities. External factors can also be important, with developments in other sectors influencing developments in terms of both supply (technological developments) and demand (exploitation opportunities). The scale and degree of synchronisation of these drivers influence the rate of industrial emergence.
Initial conditions matter: scientific and technological investments and developments that occur before a recognisable industry has emerged have a significant impact on the trajectory of emergence [12]. Understanding the history and prior evolution of an industrial area of interest is a valuable input when considering its future development, and how to navigate forward [76]. The framework for mapping industrial emergence emphasises these early phases and transitions, providing increased granularity to support decision making at this early stage, informed by the full lifecycle model incorporated into the framework.
Catalytic events: small events can have dramatic effects on industrial emergence – the ‘butterfly effect’ [77] – for example, chance conversations between key individuals.
The phases, transitions and demonstrators defined by the framework for mapping industrial emergence provide a potential basis for developing practical approaches for supporting the management of innovation emerging from the science base, at firm and sector levels. The demonstrators in particular act as a focal point for strategy development, goal setting and review, creating set of ‘stepping stones’ from science to market, as summarised in Fig. 5.
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Fig. 5 – Phases, transitions and demonstrators associated with technology-based industrial emergence
The framework for industrial emergence has been largely developed and tested through the analysis of historical cases. The diverse set of cases provides a degree of confidence that the same structures and concepts will be applicable to current and future emergence, where uncertainties are clearly much greater. Further work aims to extend the framework to application in the context of strategy and innovation, providing methods and decision support for firms and funding agencies, appropriate to the industrial context and stage of emergence. The framework provides a platform for developing several management ‘tools’ for supporting strategy and decision making, including: 1) Environmental scan: the mapping method can be used to improve understanding of the broad industrial context that influences the outcomes of investment decisions that a company or funding agency makes, combining historical data with futures information from public-domain roadmaps and other foresight reports. Such maps would be compatible with roadmapping methods, providing an improved source of market and technology intelligence for strategy processes. 2) Organisational scan: the mapping method can be used to capture and assess historical data at the organisational level, to identify strengths, weaknesses and other learning points as an input to strategy. The importance of learning from history is often largely ignored in strategic planning processes, and extending roadmaps to include the past provides a convenient means of achieving this, incorporating data from documented sources and expert knowledge collected in interviews and workshops. 3) Emergence roadmapping: the structures and principles in the framework can be directly used to enhance existing roadmapping methods for application in the early stages of industrial emergence [78,79], where uncertainties are high and the strategic impact of decisions are large. 4) Funding review criteria: the framework provides organising principles on which improved funding review criteria could be established, at both sector and firm levels, linked to the various phases and transitions, the demonstrators that demarcate them, and the thematic perspectives identified in the framework.
6.
Acknowledgements
Financial support from the UK Engineering and Physical Sciences Research Council (EPSRC) and the Gatsby Foundation is gratefully acknowledged.
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Author biographies Robert Phaal is a Senior Research Associate at the Centre for Technology Management, University of Cambridge, UK. He conducts research in the area of strategic technology management, with a particular interest in the areas of technology roadmapping and evaluation, emergence of technology-based industry and the development of practical management tools. Rob has a mechanical engineering background, with a PhD in computational mechanics, and industrial experience in technical consulting, contract research and software development. Eoin O'Sullivan is a Senior Policy Fellow at the Centre for Industry & Government, University of Cambridge, UK. He conducts research in the area of public science & innovation policy, with particular interest in the development of practical policy frameworks that underpin effective public R&D programmes. Eoin has a B.Sc. and D.Phil. in physics, and several years of experience as programme director and senior advisor to national science and innovation agencies. Michèle Routley is a Research Associate at the Centre for Technology Management, University of Cambridge, UK, investigating emergence of technology-based industries. Michèle has an MSci in Physics with Electronics, a PhD in Microelectronics, an MBA in Technology Management and several years of consultancy experience in manufacturing and business process support. Simon Ford is a Research Associate at the Centre for Technology Management, University of Cambridge, UK. His current research activities include the development of guidelines to assist firms with technology acquisitions, and understanding the complex co-evolutionary dynamics underpinning the emergence of technology-based industries. Simon holds a PhD in Engineering. David Probert is Reader in Technology Management, at the Centre for Technology Management, University of Cambridge, UK. Current research interests include technology and innovation strategy, technology management processes, technology intelligence, industry and technology evolution, software sourcing and industrial sustainability. David is currently one of five co-Investigators for the EPSRC Innovative Manufacturing Research Centre, based at the Institute for Manufacturing.
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Functional perspectives (Roadmap architecture) Typical viewpoints commercial & strategic perspectives
Design, development & production perspectives
Roadmap framework (Supports integrated and aligned strategic and innovation planning) Past
Market
Short-term
Medium-term
Time
Long-term
Route(s) forward
Why?
Business
Pull
Product What?
Service
Drivers Strategy Needs Form Function Performance
Push
Technology How?
Science Resources Three key questions:
Information types
Vision
System Technology & research perspectives
Knowledge types When?
2) Where are we now?
3) How can we get there?
1) Where do we want to go?
Fig. 1 – Roadmapping framework
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Solutions Capabilities Resources
Scale (e.g. size, sales)
Renew
Precursor Embryonic Science dominated emergence
Technology dominated emergence
Nurture Application dominated emergence
Decline Mass market
Mass market demonstrators
Price-performance demonstrators
Commercial application demonstrators
Technology Application transition
Application demonstrators
Technology demonstrators
Applied science demonstrators
Science demonstrators
Science Technology transition
Application Market transition
Disrupt / Substitute? Emergence Fail?
Growth
Mature
Decline / Renew
Market dominated emergence
Fig. 2 – Phases, transitions, milestones and trajectories of technology-intensive industrial emergence
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Time
Fig. 3 – Themes, phases and transitions for mapping industrial emergence (firm-level perspective)
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1940
17-19th C Precursor (S)
S-T
1950 Embryonic (T)
Market
WWII - US industrial diamond supply security
1960 T-A
Nurture (A)
1970
1980
A-M
Growth (M)
First Investment & expansion into existing markets diamond synthesis Norton South African by GE halts political (Carballoy synthesis situation Division) research (recruitment challenge) USA
De Beers (& GE) develop markets in partnership with tool makers (conferences, trade shows, risk sharing)
Application Pressure + temperature (gems found in volcanic rock ) Multiple failed synthesis attempts Newton (Speculation)
Simon (Oxford) persuades Openheimer to invest in diamond research High pressure physics & equipment (Bridgman, Harvard)
De Beers Research Laboratory founded (gem & industrial)
SA UK De Beers starts synthesis research
De Beers sponsors basic materials research (Oxford, Cambridge, Reading, Bristol); knowledge transfer via PhDs
‘Lost decade’ manufacturing ramp-up problems (mining company) De Beers stop research - team disperse
2000
Mature
Reduced research efforts
Decline / renew
GE sells to VC (failure); sold on to Sandvik (Sweden) De Beers market position strengthens Rebrands as Element Six
> $100M market
De Beers match GE product portfolio, and take technical & product lead
De Beers pilot plants (SA, Ireland)
Technology
1990
Composites (size, toughness, volume)
De Beers world leader in CVD - new markets (machining, surgical scalpels, telecomms, optical windows)
Rapid development due to technical & application know-how
Refocus on research (high pressure system design; materials; control; production)
De Beers first diamond synthesis
Precision, quality, cost
De Beers starts CVD applied research
Key
ASEA high pressure presses (Sweden)
Developments in finite element analysis & control systems
New MD Customer & market experience (USA) Technical Director
Lavoisier (Carbon)
Developments in laser cutting technology Developments in low pressure chemical vapour deposition technology
Fig. 4 – Emergence map for synthetic diamond industry
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Emergence stimulus Market milestones Enablers of emergence Barriers to emergence Country perspectives
Fig. 5 – Phases, transitions and demonstrators associated with technology-based industrial emergence
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